Enose Lab Made with Vacuum Sampling: Quantitative Applications

Author:

Teixeira Guilherme G.ORCID,Peres António M.ORCID,Estevinho Letícia,Geraldes Pedro,Garcia-Cabezon CristinaORCID,Martin-Pedrosa Fernando,Rodriguez-Mendez Maria LuzORCID,Dias Luís G.ORCID

Abstract

A lab-made electronic nose (Enose) with vacuum sampling and a sensor array, comprising nine metal oxide semiconductor Figaro gas sensors, was tested for the quantitative analysis of vapor–liquid equilibrium, described by Henry’s law, of aqueous solutions of organic compounds: three alcohols (i.e., methanol, ethanol, and propanol) or three chemical compounds with different functional groups (i.e., acetaldehyde, ethanol, and ethyl acetate). These solutions followed a fractional factorial design to guarantee orthogonal concentrations. Acceptable predictive ridge regression models were obtained for training, with RSEs lower than 7.9, R2 values greater than 0.95, slopes varying between 0.84 and 1.00, and intercept values close to the theoretical value of zero. Similar results were obtained for the test data set: RSEs lower than 8.0, R2 values greater than 0.96, slopes varying between 0.72 and 1.10, and some intercepts equal to the theoretical value of zero. In addition, the total mass of the organic compounds of each aqueous solution could be predicted, pointing out that the sensors measured mainly the global contents of the vapor phases. The satisfactory quantitative results allowed to conclude that the Enose could be a useful tool for the analysis of volatiles from aqueous solutions containing organic compounds for which Henry’s law is applicable.

Publisher

MDPI AG

Subject

Physical and Theoretical Chemistry,Analytical Chemistry

Reference29 articles.

1. Nariz electrónica: Herramienta para detección de gases empleando redes neuronales artificiales. Electronic nose: Tool for gas detection using Artificial Neural Networks;Eduardo;Rev. Tecnol. Digit.,2018

2. On ‘Electronic Nose’ methodology

3. A Prototype to Detect the Alcohol Content of Beers Based on an Electronic Nose

4. Electronic-nose system for classification of fruits and freshness measurement using K-NN algorithm;Kalpana;Int. J. Innov. Technol. Explor. Eng.,2019

5. Early detection of contamination and defect in foodstuffs by electronic nose: A review

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3